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Enhancing Education Through Augmented Reality: A Scoping Review of Game Elements and Pedagogical Approaches in Augmented Reality-Based Serious Games

SSRN

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SSRN 2025年

作者： Abdul Razak, Fatin Nasuha Rahman, Hameedur Kamsin, Amirrudin Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Wilayah Persekutuan Kuala Lumpur50603 Malaysia Faculty of Computing & AI Air University E9 Islamabad44400 Pakistan

The integration of augmented reality (AR) with serious games (SGs) has garnered increasing attention in educational contexts due to its potential to enhance learning outcomes and boost student engagement. However, there is a need for deeper insights into the specific pedagogical strategies and game design elements that optimize the effectiveness of AR-based SGs in educational settings. This scoping review aims to identify and categorize the key features of successful game elements and pedagogical approaches within AR-based SGs. A comprehensive multidimensional taxonomy was developed to categorize these elements. The findings reveal that AR positively influences various aspects of student learning, including behavior, cognition, emotion, motivation, skills, and overall performance. Despite these promising outcomes, the review highlights the need for further empirical studies, particularly those incorporating data science methodologies, to refine and expand the use of AR in educational games. These insights offer valuable guidance for both researchers and practitioners looking to integrate AR in SGs, advancing interactive learning and digital education. The study underscores the transformative potential of AR in education and provides a roadmap for future research to maximize its impact. © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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Mitigating Poisoning Attacks in Federated Learning Through Deep One-Class Classification

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Zhang, Anqi Zhao, Ping Lu, Wenke Zhou, Yipeng Zhang, Wenqian Zhang, Guanglin Donghua University College of Information Science and Technology Shanghai201620 China Ministry of Education Engineering Research Center of Digitized Textile and Apparel Technology Shanghai201620 China Macquarie University School of Computing Faculty of Science and Engineering 2122 Australia

Federated learning (FL) enables clients to learn a machine learning model collaboratively without sharing their private local data to the server. However, due to its distributed structure, FL is vulnerable to poisoning attacks where adversaries intentionally send the poisoned local model parameters to the server and further affect the behavior of the global model. Existing works on mitigating poisoning attacks in FL are difficult to accurately characterize data relationships of high-dimensional parameters. Moreover, they cannot guarantee Byzantine robustness when the majority of clients are compromised (Byzantine ratio) and clients’ local datasets are highly non-independent and identically distributed (non-IID). In this paper, we conduct a pioneering work to introduce deep one-class classification into mitigating poisoning attacks in FL, which can guarantee Byzantine robustness even under a Byzantine ratio and non-IID degree greater than 0.5 with the assistance of a root dataset. Our key idea is to adequately learn vector features of benign local model parameters using Deep Support Vector Data Description (Deep SVDD) and achieve the optimal classification by training a deep learning-based one-class classifier equipped with a proper decision boundary based on the root dataset. To further optimize the classifier, we employ a regularizer based on random noise injections, which can address the hypersphere collapse problem inherent in Deep SVDD. Exhaustive experiments on MNIST, F-MNIST, and CIFAR-10 demonstrate that compared with five typical Byzantine-robust methods, our defense strategy achieves excellent effectiveness in mitigating targeted/untargeted poisoning attacks and an adaptive attack in FL. Even with a similar but different root dataset, it still maintains a good Byzantine robustness. © 2015 IEEE.

关键词： Federated learning

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Leveraging retinanet based object detection model for assisting visually impaired individuals with metaheuristic optimization algorithm

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Scientific Reports 2025年第1期15卷 1-19页

作者： Khadidos, Alaa O. Yafoz, Ayman Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia King Salman Center for Disability Research Riyadh 11614 Saudi Arabia

Visually impaired individuals suffer many problems in handling their everyday activities like road crossing, writing, finding an object, reading, and so on. However, many navigation methods are available, and efficient object detection (OD) methods for visually impaired people (VIP) need to be improved. OD is the most crucial contribution of computer vision (CV) and plays an important part in recognizing and finding objects in the image. The aged and visually challenged people can identify different objects accurately under some constraint features like scaled, occlusion, illuminated, and blurred nature in different real world. A considerable number of studies are performed in the domain of real-time object recognition using deep learning (DL). The DL-based methods remove the features autonomously and classify and detect the objects. This paper proposes a novel Object Detection Model for Visually Impaired Individuals with a Metaheuristic Optimization Algorithm (ODMVII-MOA) technique. The proposed ODMVII-MOA technique aims to improve OD methods in real-time with advanced techniques to detect and recognize objects for disabled people. At first, the image pre-processing stage applies the Weiner filter (WF) method to enhance image quality by eliminating the unwanted noise from the data. Furthermore, the RetinaNet technique is utilized for the OD process to recognize and locate objects within an image. Besides, the proposed ODMVII-MOA method employs the EfficientNetB0 method for the feature extraction process. For the classification process, the LSTM-AE method is employed. Finally, the Dandelion Optimizer (DO) method adjusts the hyperparameter range of the LSTM-AE method optimally and results in better performance of classification. The experimental validation of the ODMVII-MOA model is verified under the indoor OD dataset and the outcomes are determined regarding different measures. The comparison study of the ODMVII-MOA model portrayed a superior accuracy value of 99.6

关键词： Computer vision Dandelion optimizer Deep learning Feature extraction Object detection Visually impaired individuals

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DPS: Design Pattern Summarisation Using Code Features

arXiv

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arXiv 2025年

作者： Nazar, Najam Sikka, Sameer Treude, Christoph School of Computer and Mathematical Sciences The University of Adelaide Australia School of IT and Engineering Ada University Azerbaijan Faculty of Engineering and Information Technology The University of Melbourne Australia School of Computing and Information Systems Singapore Management University Singapore

Automatic summarisation has been used efficiently in recent years to condense texts, conversations, audio, code, and various other artefacts. A range of methods, from simple template-based summaries to complex machine learning techniques—and more recently, large language models—have been employed to generate these summaries. Summarising software design patterns is important because it helps developers quickly understand and reuse complex design concepts, thereby improving software maintainability and development efficiency. However, the generation of summaries for software design patterns has not yet been explored. Our approach utilises code features and JavaParser to parse the code and create a JSON representation. Using an NLG library on this JSON representation, we convert it into natural language text that acts as a summary of the code, capturing the contextual information of the design pattern. Our empirical results indicate that the summaries generated by our approach capture the context in which patterns are applied in the codebase. Statistical evaluations demonstrate that our summaries closely align with human-written summaries, as evident from high values in the ROUGE-L, BLEU-4, NIST, and FrugalScore metrics. A follow-up survey further shows that DPS summaries were rated as capturing context better than human-generated summaries. Copyright © 2025, The Authors. All rights reserved.

关键词： Software design

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Multi-Dimensional Spectrum Prediction Method Based on Efficient Adaptive Broad Learning

Multi-Dimensional Spectrum Prediction Method Based on Effici...

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IEEE Conference on Wireless Communications and Networking

作者： Niancong Ji Shufei Wang Yibin Zhang Yang Peng Tomoaki Otsuki Dusit Niyato Guan Gui College of Telecommunications and Information Engineering NJUPT Nanjing China Faculty of Science and Technology Keio University Kanagawa Japan College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Multi-dimensional spectrum prediction is essential for spectrum sharing and dynamic spectrum access (DSA), tack-ling spectrum scarcity and improving wireless communication. Traditional methods often use machine learning (ML), which requires manual feature extraction, or deep learning (DL), which demands high computational resources. This paper proposes a lightweight multi-dimensional spectrum prediction model using an adaptive broad learning network (ABLN). The model employs a sliding window to preprocess data and establishes input layers using randomly generated feature and enhancement nodes. The weights of broad learning are determined by solving the pseudo-inverse, and the structure is incrementally extended without retraining, reducing computational complexity. An adaptive node increment module optimizes hyperparameters efficiently. Experimental results demonstrate that ABLN reduces computational overhead while maintaining robust prediction performance across various scenarios.

关键词： Wireless communication Adaptation models Adaptive systems Computational modeling Dynamic spectrum access Training data Manuals Predictive models Data models Robustness

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Reinforcement Learning for Intrusion Detection: Recent Advances and Datasets

Reinforcement Learning for Intrusion Detection: Recent Advan...

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Advanced Innovations in Smart Cities (ICAISC), International Conference on

作者： Dalal Fathi Afraa Attiah Abeer Hakeem Asma Cherif IT Department Faculty of Computing and Information Technology Kind Abdulaziz University Jeddah Saudi Arabia Center of Excellence in Smart Environment Research Kind Abdulaziz University Jeddah Saudi Arabia

ISBN: (数字)9798331506995

ISBN: (纸本)9798331507008

With the development of technologies and increasing security threats, intrusion detection systems have become more critical in detecting and protecting operations from attacks. Deep learning has significantly contributed to advancements in intrusion detection, especially through reinforcement learning systems. This survey reviews the concepts of intrusion detection and Reinforcement Learning (RL) systems in intrusion detection (ID), with a focus on recent advancements using techniques such as Multi-Agent Reinforcement Learning (MARL), Adversarial Reinforcement Learning (AE-RL), and Inverse Reinforcement Learning (IRL). We also emphasize the crucial role of feature engineering in conjunction with RL techniques. By examining these cutting-edge approaches and their integration with advanced feature engineering methods, we aim to provide a comprehensive overview of the current state of the art in reinforcement learning-based intrusion detection systems and their potential to enhance cybersecurity measures. In addition to exploring the applications of reinforcement learning and feature engineering in intrusion detection, we highlight and analyze the most well-known databases used in this field, offering insights into the data resources that drive the development and evaluation of these advanced security systems.

关键词： Surveys Training Deep learning Technological innovation Smart cities Reviews Intrusion detection Reinforcement learning Feature extraction Computer security

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Data Generation in Healthcare Using Constraint Satisfaction Parameters (CSP)

Data Generation in Healthcare Using Constraint Satisfaction ...

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Advanced Innovations in Smart Cities (ICAISC), International Conference on

作者： Hassanin M. Al-Barhamtoshy Tarik Himdi Information Technology Dept. Faculty of Computing King Abdulaziz University Jeddah Saudi Arabia Computer Science Dept. Jeddah International College Jeddah Saudi Arabia

ISBN: (数字)9798331506995

ISBN: (纸本)9798331507008

In this research, we examine the synthetic creation, alteration, and enhancement of data through Generative Artificial Intelligence (GAI). This inaugural study focused on generating healthy data via GAI for analysis and detection of chronic diseases (Depression, Stress, and Anxiety). Following the generation process using this deep learning approach, we analyze and visualize synthetic data through various metrics. The outcomes of this methodology are expected to yield novel insights into data generation concerning pressure-related diseases (depression, stress, and anxiety). Integrating high-quality synthetic data is anticipated to improve the production of financial transaction data. Finally, the proposed model is evaluated, and the results of the initial GAI model are generated. In this paper, we introduce a specialized generative Transformer language model that has been pre-trained on an extensive collection of medical constraint satisfaction parameters. We assess generation across medical natural language processing tasks and show that it surpasses existing models in most evaluations. Additionally, we offer a comprehensive analysis of the model's limitations by detailing numerous errors and challenges it faces. The mean scores for the produced descriptive data and shape pair trends in continuous columns were around 0.99 and 0.92, respectively. Conversely, the average scores for the categorical text generated in the initial region were notably high. This highlights the GAI model utilized in implementing each method. Our evaluation revealed that a majority of studies employ synthetic data generators to (i) reduce the costs and duration linked to clinical trials for rare diseases, (ii) enhance the predictive performance of AI models in personalized medicine, and (iii) provide researchers with access to high-quality, representative multimodal datasets while protecting sensitive patient information, among other advantages. Synthetic data generators based on deep learn

关键词： Deep learning Technological innovation Generative AI Anxiety disorders Data collection Depression Transformers Generators Synthetic data Diseases

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Improved in Silico Identification of Protein-Protein Interactions Using Deep Learning Approach

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IET Systems Biology 2025年第1期19卷

作者： Khan, Irfan Arif, Muhammad Ghulam, Ali Albaradei, Somayah Thafar, Maha A. Worachartcheewan, Apilak Department of Computer Science Abdul Wali Khan University Mardan KPK Mardan Pakistan College of Science and Engineering Hamad Bin Khalifa University Doha Qatar Information Technology Centre Sindh Agriculture University Tandojam Pakistan Department of Computer Science Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Department of Computer Science College of Computers and Information Technology Taif University Taif Saudi Arabia Department of Community Medical Technology Faculty of Medical Technology Mahidol University Bangkok Thailand

Protein–protein interactions (PPIs) perform significant functions in many biological activities likewise gene regulation, metabolic pathways and signal transduction. The deregulation of PPIs may cause deadly diseases, such as cancer, autoimmune, pernicious anaemia etc. Detecting PPIs can aid in elucidating the cellular process's underlying molecular mechanisms and contribute to facilitating the discovery of new proteins for the development of novel drugs. Although high-throughput wet-lab technologies have been matured to identify large scale PPI identification;however, the traditional experimental methods are costly and slow and resource intensive. To support experimental techniques, numerous computational approaches have been emerged for identifying PPIs solely from protein sequences. However, the performance of available PPI tools are unsatisfactory and gaps remain for further improvement. In this study, a novel deep learning-based model, Deep_PPI, was developed for predicting multiple species PPIs. To extract the biological features, the authors used 21D vector representing 20 kinds' native and one special amino acid residue and implemented the Keras binary profile encoding technique to formulate each residue in proteins. The binary profile use the PaddVal strategy to equalise the length of positive and negative PPIs. After extracting the features, the authors fed them into one dimension convolutional neural network to build the final prediction model. The proposed Deep_PPI model, which consider the protein pairs into two convolutional heads. Finally, the authors concatenated the two outputs were concatenated from two branches concatenated by fully connected layer. The efficiency of the proposed predictor was demonstrated both on the cross validation and tested on various species datasets, for example, that is (Human, C. elegans, E. coli, and H. sapiens). The proposed model surpassed both the machine-learning models and existing state-of-the-art PPI methods. The

关键词： Proteomics

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Interactive AI for Advancing and Generating Women's Traditional Fashion in Saudi Arabia

Interactive AI for Advancing and Generating Women's Traditio...

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Advanced Innovations in Smart Cities (ICAISC), International Conference on

作者： Hassanin M. Al-Barhamtoshy Rania M. Deabes Fardus A. Baalamash Faculty of Computing & Information Technology King Abdulaziz University Kingdom of Saudi Arabia Faculty of Human Sciences and Design King Abdulaziz University Kingdom of Saudi Arabia College of Design and Arts Umm Al-Qura University

ISBN: (数字)9798331506995

ISBN: (纸本)9798331507008

This research sought to safeguard the cultural legacy of women's traditional attire in the Kingdom of Saudi Arabia by developing a comprehensive digital platform for the collection and categorization of these costumes. The emphasis was placed on the Western region of Saudi Arabia, where a dataset of traditional garments was collected, compiled, and generated. Moreover, a specialized website was created to streamline the marketing and purchasing processes for these traditional outfits. The methodology integrated both descriptive-analytical and experimental approaches based on generative AI. Tools utilized in this research included an expert opinion survey aimed at understanding the requirements for implementing an artificial intelligence-based classification system for women's traditional clothing, as well as measuring user perceptions regarding the website's effectiveness in marketing Saudi Arabia's traditional wear. Additionally, questionnaires were distributed to gather input from specialists concerning the classification system and its related website, focusing on advancing production and marketing strategies. Users' feedback on both the AI-driven classification system and the website was also collected. Key findings indicated that it is viable to preserve cultural heritage represented by traditional garments from Saudi Arabia's western region through their collection and organization within an AI-based framework. Furthermore, this study highlighted opportunities for promoting and selling traditional fashion via a website equipped with an AI classification system. The AI-driven classification system demonstrated remarkable accuracy, approaching 97%, with an error rate of around 3.15%. The conclusion includes recommendations for sharing this system with fashion houses interested in producing traditional women's clothing in Saudi Arabia, along with providing access to databases that could assist in designing and manufacturing such apparel.

关键词： Surveys Technological innovation Accuracy Smart cities Clothing Symbols Production Organizations Cultural differences Convolutional neural networks

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Evaluating Reinforcement Learning Reward Functions for APT Detection in Industrial IoT Systems

Evaluating Reinforcement Learning Reward Functions for APT D...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Mohammad Alauthman Amjad Aldweesh Ahmad Al-Qerem Ibtisal Daoud Mouhammd Alkasassbeh Amjad Gawanmeh Department of Information Security University of Petra Jordan College of computing and IT Shaqra University Shaqra Saudi Arabia Faculty of Information Technology Zarqa University Zarqa Jordan Computer Science Department Princess Sumaya University for Technology Jordan University of Dubai United Arab Emirates

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Advanced Persistent Threats (APTs) pose significant challenges in Industrial Internet of Things (IIoT) environments, as they often employ stealthy and multi-stage attack strategies. The complexity of heterogeneous data sources, including provenance and network logs, further complicates detection. In this paper, we leverage the newly introduced CICAPT-IIoT2024 dataset as a benchmark for evaluating Intrusion Detection Systems (IDS). We focus on three Reinforcement Learning (RL) reward functions—Weighted Precision and Recall, Cost-Sensitive, and Balanced Accuracy—to determine their effectiveness in detecting APTs. Our experiments show that selecting the right reward function greatly influences detection trade-offs such as precision, recall, and false positive rates. The findings highlight the nuanced role of reward tuning and emphasize that a cost-sensitive approach more effectively penalizes false negatives in high-stakes IIoT scenarios, whereas balanced accuracy rewards maintain a more uniform trade-off between detection and false alarms. We discuss the implications of these outcomes for real-world IIoT deployments and propose directions for future research in improving RL-based IDS performance.

关键词： Accuracy Soft sensors Intrusion detection Reinforcement learning Benchmark testing Complexity theory Tuning Computational intelligence Industrial Internet of Things

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